Energy and Power Engineering, 2010, 2, 73-77
doi:10.4236/epe.2010.22011 Published Online May 2010 (
Copyright © 2010 SciRes. EPE
Dopant Implantation into the Silicon Substrate with
Non-Planar Surface
Gennady A. Tarnavsky1, Evgenii V. Vorozhtsov2
1Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of the Russian Academy of
Sciences, Novosibirsk, Russia
2Khristianovich Institute of Theoretical and Applied Mechanics, Siberian Branch of the Russian Academy of Sciences,
Novosibirsk, Russia
Received January 3, 2010; revised February 9, 2010; accepted March 29, 2010
The influence of technological process parameters (aiming angle and implantation energy) on the distribu-
tions of dopant concentrations in a silicon substrate is investigated by computer modeling.
Keywords: Computer Modeling, Silicon Doping, Implantation, Donor, Acceptor Dopants
1. Introduction
Advanced technologies of the design and manufacturing
of nano-electromechanical systems (NEMS technologies)
are based on complex physical, chemical, and mechani-
cal processes. These processes constitute a chain of base
stages (segments) of the industrial production of various
NEMS devices.
The doping is one of important technological proc-
esses. This is the process of adding the dopants of the
acceptor and/or donor type (phosphorus, arsenic, anti-
mony, boron, gallium, etc.) to a silicon plate for the pur-
pose of creating the zones of increased conductivity of
the n- and p-type in semiconductor for the arrangement
of the diode and transistor channels.
The doping can, besides, be aimed at achieving some
technological goals. For example, the doping by boron
stops the process of the basic material etching.
The ion implantation, which consists of the introduc-
tion in the semiconductor material of ionized atoms ac-
celerated in the electric field and possessing a high en-
ergy is at present the most efficient and widely used
doping technology. This technology requires specialized
complex setups, but it is much more accurate than the
diffusion method, and it is applied for creating the hid-
den layers, the p- and n-pockets, domains in the chip,
which form the transistor channels.
Computer experiments, which are more mobile and
much cheaper in comparison with the physical ones (if
one does not account for the expenses of intellectual ef-
forts), with a possibility of considering hundreds and
even thousands of variants, with a rapid and efficient
analysis of their results as well as with a capability of
increasing the levels of the employed physical and
mathematical models, play an increasing role in the de-
sign of new semiconductor materials.
In the present work, the influence of technological
process parameters (aiming angle and the implantation
energy) on the distributions of dopant concentrations in a
silicon substrate is investigated by computer modeling
based on a new mathematical formulation proposed by
one of the present authors (G.A.T.).
2. Computer Toolbox
The program complex NanoMod [1] is intended for
conducting the scientific research and applied develop-
ments in the field of microelectronics, including the mi-
croprocessor technology. The complex may be used for
teaching the specialists, postgraduates, and students in
the given knowledge area.
The computer toolbox NanoMod has been developed
on the basis of advanced original algorithms for model-
ing the electrophysical, thermochemical, and mechanical
processes (see [2] and the bibliography therein) for
computer support of the automated design of nano-
structured semiconductor materials.
In the present work, we present the results obtained by
using one of the segments of the program complex—the
IMPL solver (IMPLantation).
The work was financially supported by the Russian Foundation for
Basic Research (grant No. 08-07-12001-ofi).
2.1. The IMPL Solver
The algorithm of the segment is based on the method for
numerical computation of the Gauss law for the distribu-
tion of dopant concentrations of various elements intro-
duced in a silicon substrate by the ion implantation tech-
nique (see [3]):
()exp(() )CAB
 . (1)
The coordinate direction ξ in (1) is the direction of
ions motion from the acceleration device to the substrate.
This direction is generally not perpendicular to the sub-
strate surface but makes the angle αI with the normal to it.
The ξ coordinate is measured from the point of the start
of the ion introduction into the substrate with the in-
creasing ξ value at the motion deep into the material. The
quantities A, B, and ξ0 are the constants depending on
physical factors—the type SI and the dose DI of the im-
plant, the energy of ions acceleration EI :
(,, )
ASD E, (,, )
(,, )
. (2)
The quantity ξ0 in (2) is the depth of the maximum in-
troduction of ions into the substrate—the peak of the
Gauss distribution (1) with the dopant concentration,
which drops exponentially deep into the material and
towards its surface.
In the presence of protective masks on the substrate
surface, which are opaque for the flux of implanted ions,
one can account for the dopant propagation in the tan-
gential direction under the mask along the second coor-
dinate direction
normal to
(see the details in [4]):
()erf( ())CFk
 , (3)
(,, )
FSD E, (,, )
(,, )
. (4)
The quantities F, k, and
0 in (3), (4) have the follow-
ing meaning. The parameter
0 is the coordinate of the
mask edge, from which the value of the
coordinate is
measured (under the mask), and parameters F and k are
the constants depending on the physical parameters of
the problem.
The resulting concentration is expressed in the form
(,)() ()CCC
. (5)
In the (x,y) coordinates of real problems at a
non-planar substrate surface and/or nonzero value of the
implantation aiming angle, the function C(x,y) in (5)
cannot be presented in the form of a splitting into the
product of C1(x) and C2(y). Because of this, the numeri-
cal algorithm for computing the problem (1)-(5) in
)-coordinates is used in the solver. This solution is
then represented in the (x,y)-coordinates.
The values of quantities SI, DI, EI, and αI are input by
the user in accordance with the problem formulation. The
values of parameters A, B, F, k, ξ0, and
0 are contained
in a specialized database of the complex and are input by
the system for supporting the tasks without involving the
It is to be noted that the completeness of tables of
these parameters and their numerical values, which are
permanently refined in physical experiments (executed
on the advanced research setups) and which are input in
a timely fashion in the solver database, increases the
computer toolbox efficiency.
3. Results of Computer Modeling
3.1. Problem Formulation
The solution of problem (1)-(5) is to be found in the re-
gion R:
0,yy, (6)
consisting of two subregions—the external medium
subregion RV and the silicon subregion RSi, which are
separated by the boundary
Yx. (7)
It is assumed here that the doping is made only in RSi
that is at y < YSi, and the concentrations of implanted
dopants are equal to zero in RV.
A discrete computational grid with the number of
nodes Nx and Ny along the x- and y-directions, respec-
tively, is generated in (6) for the numerical solution of
problem (1)-(5). The appropriate values of Nx and Ny
were determined within the framework of the grid re-
finement study as follows: several runs were done with
sequentially increasing values 1 < Nx
(1) <…< Nx
(k–1) <
(k), Ny
(1) <…< Ny
(k–1) < Ny
(k), and the results obtained for
sequential pairs (Nx
(k–1), N
(k–1)) and (Nx
(k), N
(k)) were
compared with one another. As soon as the difference in
the results obtained for these pairs becomes sufficiently
small, the last pair (Nx
(k), Ny
(k)) is chosen as the final one
for the modeling. In the scope of problems presented
below the modeling was carried out in the region with
xmax = 100 nm and ymax = 60 nm. For this region, the grid
with Nx = 151 and Ny = 151 was found to be appropriate.
The following values of problem parameters were used
in (2) and (4): the implantation of phosphorus P, boron B,
and arsenic As was investigated (parameter SI), the im-
plant dose was fixed (cm–3), and the implanta-
tion energy EI was varied from 5 eV to 20 eV.
The values of concentrations obtained in computations
are presented in dimensional units [cm–3].
Copyright © 2010 SciRes. EPE
Figure 1. Implantation of dopant donor and acceptor admixtures into the silicon Si substrate with planar surface. The proc-
ess scheme (а), the distributions of the concentrations of the arsenic As (b) and boron B (c). Implantation energy 15 eV.
Figure 2. Phosphorus P implantation into the silicon Si substrate with planar surface. Implantation energy 15 eV. Distribu-
ions of concentrations of P in Si at the aiming angle of implantation 0° (а), 30° (b), and 60° (c).
3.2. Implantation into the Silicon
with Planar Surface
Consider the results of problem solution at a constant
value of in (7), the implantation energy 5
eV, and three doping types: As, B, and P.
Figure 1 illustrates the results of computing the seg-
ment. The implantation scheme and the distribution of
the arsenic As and boron B concentrations in the silicon
Si are shown.
Note that for the same implantation energy, the pene-
tration depth of B in Si is higher than the penetration
depth of As in Si. The maximum of the As distribution
takes place at y = 20 (at the depth of 15 nm from the sur-
face), and in the case of B, it takes place at y = 0 (the
depth of 35 nm).
The maximum of the P concentration (Figure 2(а))
lies at the depth of 20 nm (y = 15). Thus, the phosphorus
penetrates the silicon deeper than the arsenic for the same
implantation energy, but it yields to boron in terms of this
The implanted layer width is the largest in the case of
As, and it is the least in the case of B. The process of
dopant diffusion with time spreads this distribution at a
speed depending on several factors (the temperature, the
crystal lattice type, etc.).
Let us analyse the influence of the implantation aim-
ing angle
. Figure 2 shows the results of computing
the phosphorus implantation for the values 0
and 60 (Figures 2(a), (b), and (c), respectively). The
is measured from coordinate lines x = const,
in the given problem it is measured from the direction
normal to the basic substrate surface.
One can predict a priori that as a result of modeling
the 2D problem, one must obtain the 1D structures of the
distribution of concentrations C = f (y), and such a prob-
lem statement is, besides the obtaining of scientific data,
a test for the correctness of the functioning of the com-
putational algorithm and the computer code implement-
ing it.
Computed results correspond to the predictions: the
distributions of phosphorus concentrations depend only
on the y-coordinate.
The following fact takes place here: with increasing
, the penetration depth of P in Si drops, and it
is naturally directly proportional to cos
, which is
seen in Figure 2.
The distribution functions , where
considered as a parameter, correlate with one another
under the variation of
with small differences.
So the function
60 ,C
y has a narrower localiza-
tion than
, and its maximum is by about 15%
higher. The form and the digital values of function
y30 ,C
take the intermediate position between
y and
60 ,Cy
The differences in functions are due to the proximity
of the Si boundary to the y-coordinate of the maximum
of the Gauss distribution (1), and the “tail” of this distri-
bution is “cut off” by the substrate surface.
3.3. Implantation into a Substrate with
Non-Planar Surface
Consider the results of computer modeling of the phos-
opyright © 2010 SciRes. EPE
phorus P implantation into the silicon Si with a
non-planar surface under the variation of the aiming an-
and the activation energy EI.
Figure 3(a) shows the configuration (the sizes are in
nm) of the Si base substrate with the grooves cut therein.
The stage of the technological cycle of operations with
such configurations often takes place at the formation of
semiconductor materials with given electrophysical pro-
perties of NEMS units, in particular, the elements of
three-gate nano-transistors of SON designs (“silicon-on-
nothing”, see, for example, [5,6]).
Figures 3(b) and (c) show the distributions of C(x,y)
for the values and , respectively.
At , the dopants form the domains with a nar-
row localization, whose sizes are of the order of 5-10 nm
and maxima of the penetration depth of about 7-8 nm
(for the given implantation energy EI = 5 eV. Note that in
the zones of deep grooves, the implant reaches the sub-
strate lower edge (the bottom).
At the implantation under a larger angle
(Figure 3(c)) the doping domains naturally “turn” to the
implant flow direction.
At such a nano-relief of the surface, shadow zones
arise in the substrate. So the concentration of P is insig-
nificant in the left Si bump, and on the boards of the
grooves and at the plate bottom, the concentration C(x,y)
is nearly equal to zero.
Figure 4 illustrates the influence of the implantation
energy EI at a fixed aiming angle and the
same nano-relief of the surface. An increase in EI from 5
eV (Figure 4(a)) to 10 eV (Figure 4(b)) and so on, up to
15 eV (Figure 4(c)) alters significantly the pattern of
the P distribution in Si.
The location, sizes, and intensity of doping spots
prove to be variable. So, in particular, a doping domain
arises at EI = 5 eV in the central bump. Although it has a
low intensity and a small size, it is well noticeable. At EI
= 10 eV, this domain is smeared significantly, and at EI =
15 eV, the central bump is “transparent” for the implant
flux, which “passes” through it and locates in the left
segment of the substrate configuration.
The concentration of P in the left segment depends
substantially on EI and alters from a small domain (Fig-
ure 4(a)) to the formation of a vast zone (Figure 4(c)).
The right segment of the substrate configuration, as
the non-obscured one, is more conservative to the EI
variation: the doping domain has here approximately
equal sizes, but with increasing EI it naturally shifts deep
into the substrate down to its very bottom (Figure 4(c)).
Note in conclusion that the function C(x,y) determines
the arrangement in Si of the zones of an elevated electric
conductivity, in which the arrangement of the channels of
nano-transistors is possible (see, for example, [6,7]).
3.4. Miniaturization of NEMS: Problems of
Mathematical Modeling
The improvement of industrial production of the elec-
tronics and the passage, during the last 5 years, from the
130 nm technologies to 90 nm, 65 nm, 45 nm, 32 nm,
and 22 nm technologies puts in foreground new very
complex problems of the mathematical modeling of
electrophysical, thermochemical, and mechanical proc-
esses, which are used in these technologies, within the
framework of a continuum model.
So the computational region size of the problem R,
(a) (b) (c)
Figure 3. Phosphorus P implantation into the silicon Si substrate with a non-planar surface. The substrate configuration (а),
distributions of the concentrations of P in Si at the aiming angle of implantation 0° (b) and 60° (c). Implantation energy 5 eV.
(a) (b) (C)
Figure 4. Phosphorus P implantation into the silicon Si substrate with non-planar surface. Distributions of the concentrations
of P in Si at the aiming angle of implantation 30° and the implantation energy 5 eV (а), 10 eV (b), and 15 eV (c).
Copyright © 2010 SciRes. EPE
amounts, as a rule, already to about 100 nm (it is differ-
ent in different coordinate directions depending on prob-
lem formulation).
Let us write down some physical characteristics of the
silicon Si (at the temperature of about 300 deg. К and the
pressure of 1 atm):
– 12 nm, the de Broile wave length (L1);
– 0.54 nm, the crystal lattice step (L2);
– 0.12 nm, the atom size according to Melvin-Hughes
Comparing the R values with the values of L1, L2, and
L3 one can draw some important conclusions.
First, since R is higher than L1 (the conditional bound-
ary of the onset of the manifestation of quantum effects
in Si), much higher than L2, and much higher than L3, then
the continuum model is (still) well valid and applicable.
Second, there takes place, however, the following. Let
a grid of N nodes be used in computation. The grid step
is then d = R / N. At R = 100 nm and N = 100 we have d
= 1 nm. Thus, the grid step d is less than L1, is compara-
ble with L2, and is higher than L3.
While aiming at increasing the accuracy of computa-
tion in the “classical” cases (we leave aside the bifurca-
tion situation) one should increase N, for example, up to
1000 nodes. In this case, the grid step d equals 0.1 nm,
and it is comparable with the silicon atom size L3 and by
a factor of five less than L2, that is at the grid generation,
each four nodes of five get into the inter-atomic space of
the crystal lattice.
Thus, it becomes indefinite, what a process is modeled.
Generally speaking, these considerations are not quite
correct because an “averaging” of the real substance in
which there are neither atoms nor their groups in the lat-
tice is assumed at the use of the continuum model, where
one can formulate a continuous differential problem in
which the processes are described by differential equa-
tions, for example, diffusion equations with empirical
coefficients obtained from reality (the standard or pre-
stressed silicon, temperature, etc.).
The discretization of a continuous problem (the com-
putational algorithm construction) naturally introduces
its problems (approximation, convergence, solution uni-
queness). These problems, however, have quite a differ-
ent character and have nothing common to do with the
problems of the adequacy of the continuum model as
applied to real processes.
We can summarize the above said as follows. The
model of continuum on the sizes of 100 nm is, on the
whole, still sufficiently correct, but the local modeling of
structures already requires a special analysis and the cau-
tion at the interpretation of obtained numerical results.
The advanced technologies enable the manufacturing
of NEMS with sizes of elements less than 10 nm. So the
two-layer gate dielectrics are used in the NEMS transis-
tors. The first layer is a thermal silicon dioxide SiO2 1
nm or less thick. The second layer is the silicon nitride
Si3N4 (or a mixture of the oxide and nitride); it has about
the same thickness. At such sizes, the accuracy of com-
puting the oxidation process within the framework of
the continuum model becomes very questionable. It is
required to use more complex physical and mathe-
matical models and the computational algorithms im-
plementing them.
A passage to the quantum-mechanical model, which
necessitates the derivation of the Hamilton operators and
the solution of the Schroedinger equation, will require
the development of new theoretical methods, computa-
tional algorithms, and the application of high-perform-
ance supercomputers.
4. Conclusions
In the present work, a computer modeling of one of the
important technological processes applied at the design
of new semiconductor materials with given electrophy-
sical properties—the process of the implantation of
dopants into the base silicon substrate with a non-planar
surface has been carried out.
The influence of process parameters (the aiming angle
and implantation energy) on the distributions of concen-
trations of the admixtures of phosphorus, boron, and ar-
senic in silicon has been investigated.
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